NYU Depth

NYU Depth Dataset V2: Indoor Segmentation and Support Inference from RGBD Images. We present an approach to interpret the major surfaces, objects, and support relations of an indoor scene from an RGBD image. Most existing work ignores physical interactions or is applied only to tidy rooms and hallways. Our goal is to parse typical, often messy, indoor scenes into floor, walls, supporting surfaces, and object regions, and to recover support relationships. One of our main interests is to better understand how 3D cues can best inform a structured 3D interpretation. We also contribute a novel integer programming formulation to infer physical support relations. We offer a new dataset of 1449 RGBD images, capturing 464 diverse indoor scenes, with detailed annotations. Our experiments demonstrate our ability to infer support relations in complex scenes and verify that our 3D scene cues and inferred support lead to better object segmentation.


References in zbMATH (referenced in 11 articles )

Showing results 1 to 11 of 11.
Sorted by year (citations)

  1. Lienen, Julian; Hüllermeier, Eyke: Instance weighting through data imprecisiation (2021)
  2. Chai, Dengfeng: Rooted spanning superpixels (2020)
  3. Rosu, Radu Alexandru; Quenzel, Jan; Behnke, Sven: Semi-supervised semantic mapping through label propagation with semantic texture meshes (2020)
  4. Valada, Abhinav; Mohan, Rohit; Burgard, Wolfram: Self-supervised model adaptation for multimodal semantic segmentation (2020)
  5. Tang, Meng; Marin, Dmitrii; Ben Ayed, Ismail; Boykov, Yuri: \textitKernelcuts: kernel and spectral clustering meet regularization (2019)
  6. Liu, Weiwei; Tsang, Ivor W.; Müller, Klaus-Robert: An easy-to-hard learning paradigm for multiple classes and multiple labels (2017)
  7. Hasnat, Md. Abul; Alata, Olivier; Trémeau, Alain: Model-based hierarchical clustering with Bregman divergences and fishers mixture model: application to depth image analysis (2016)
  8. Khan, Salman H.; Bennamoun, Mohammed; Sohel, Ferdous; Togneri, Roberto; Naseem, Imran: Integrating geometrical context for semantic labeling of indoor scenes using RGBD images (2016)
  9. Diebold, Julia; Demmel, Nikolaus; Hazırbaş, Caner; Moeller, Michael; Cremers, Daniel: Interactive multi-label segmentation of RGB-D images (2015)
  10. Hazırbaş, Caner; Diebold, Julia; Cremers, Daniel: Optimizing the relevance-redundancy tradeoff for efficient semantic segmentation (2015)
  11. Silberman, Nathan; Hoiem, Derek; Kohli, Pushmeet; Fergus, Rob: Indoor segmentation and support inference from RGBD images (2012) ioport